Matched-Control Quasi-Experiment
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A Matched-Control Quasi-Experiment is a Quasi-Experiment where treated unit is matched to a non-treated unit with similar observable characteristics against whom the effect of the treatment can be assessed.
- Example(s):
- Counter-Example(s):
- See: Age-Matched Quasi-Experiment, Unmatched-Control Quasi-Experiment.
References
2013
- http://en.wikipedia.org/wiki/Matching_%28statistics%29
- Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment without reduced bias due to confounding.[1][2][3] Propensity score matching, an early matching technique, was developed as part of the Rubin Causal Model model.[4]
Matching has been promoted by Donald Rubin.[5] It was prominently been criticized in economics by LaLonde (1986),[6] who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Dehejia and Wahba (1999) reevaluted LaLonde's critique and show that matching is a good solution.[7] Similar critiques have been raised in political science[8] and sociology[9] journals.
- Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment without reduced bias due to confounding.[1][2][3] Propensity score matching, an early matching technique, was developed as part of the Rubin Causal Model model.[4]
- ↑ Rubin, Donald B. (1973). "Matching to Remove Bias in Observational Studies". Biometrics 29 (1): 159–183. doi:10.2307/2529684. JSTOR 2529684.
- ↑ Anderson, Dallas W.; Kish, Leslie; Cornell, Richard G. (1980). "On Stratification, Grouping and Matching". Scandinavian Journal of Statistics 7 (2): 61–66. JSTOR 4615774.
- ↑ Kupper, Lawrence L.; Karon, John M.; Kleinbaum, David G.; Morgenstern, Hal; Lewis, Donald K. (1981). "Matching in Epidemiologic Studies: Validity and Efficiency Considerations". Biometrics 37 (2): 271–291. doi:10.2307/2530417. JSTOR 2530417. PMID 7272415.
- ↑ PR Rosenbaum; DB Rubin (1983). "The central role of the propensity score in observational studies for causal effects". Biometrika.
- ↑ PR Rosenbaum; DB Rubin (1983). "The central role of the propensity score in observational studies for causal effects". Biometrika.
- ↑ LaLonde, Robert J. (1986). "Evaluating the Econometric Evaluations of Training Programs with Experimental Data". The American Economic Review 76 (4): 604–620. JSTOR 1806062.
- ↑ RH Dehejia; S Wahba (1999). "Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs". Journal of the American Statistical Association..
- ↑ Arceneaux, Kevin, Alan S. Gerber, and Donald P. Green. 2006. “Comparing Experimental and Matching Methods Using a Large-Scale Field Experiment on Voter Mobilization.” Political Analysis 14 (1) (May): 37–62. DOI: 10.1093/pan/mpj001. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.136.8988&rep=rep1&type=pdf.
- ↑ Arceneaux, Kevin, Alan S. Gerber, and Donald P. Green. 2010. “A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark.” Sociological Methods & Research 39 (2) (August 10): 256–282. DOI: 10.1177/0049124110378098. http://smr.sagepub.com/cgi/doi/10.1177/0049124110378098.
2011
- (Roser, Fiser et al., 2011) ⇒ Matthew E. Roser, József Fiser, Richard N. Aslin, and Michael S. Gazzaniga. (2011). “Right Hemisphere Dominance in Visual Statistical Learning.” In: Journal of Cognitive Neurosci, 23(5). doi:10.1162/jocn.2010.21508
- ABSTRACT: Several studies report a right hemisphere (RH) advantage for visuo-spatial integration and a left hemisphere (LH) advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual-feature combinations. A split-brain patient and normal control participants viewed multi-shape scenes presented in either the right or left visual fields. Unbeknownst to the participants the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and test displays were presented in the left visual field (RH). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning.
… Data from the group of age-matched adult participants are presented in Figure 4 and are labeled as the Old group. The age-matched control group performed better than chance in all four test conditions … Instructions to Age-Matched Control participants. …
- ABSTRACT: Several studies report a right hemisphere (RH) advantage for visuo-spatial integration and a left hemisphere (LH) advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual-feature combinations. A split-brain patient and normal control participants viewed multi-shape scenes presented in either the right or left visual fields. Unbeknownst to the participants the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and test displays were presented in the left visual field (RH). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning.